Objective

2

Fine resolution images with high acquisition frequency play a key role in earth surface observation. However

due to technical and budget limitations

current satellite sensors have a tradeoff between spatial and temporal resolutions. No single sensor can simultaneously achieve a fine spatial resolution and a frequent revisit cycle although a large number of remote sensing instruments with different spatial and temporal characteristics have been launched. For example

Landsat sensors have fine spatial resolutions (15~60 m) but long revisit frequencies (16 days). By contrast

a moderate resolution imaging spectro-radiometer (MODIS) instrument has a frequent revisit cycle (1 day) but a coarse spatial resolution (250~1 000 m). In addition

optical satellite images are frequently contaminated by clouds

cloud shadows

and other atmospheric conditions. These factors limit applications that require data with both high spatial resolution and high temporal resolution. Spatio-temporal satellite image fusion is an effective way to solve this problem. Many spatio-temporal fusion methods have been proposed recently. Existing spatio-temporal data fusion methods are mainly divided into the following three categories:weight function-based methods

unmixing-based methods

and dictionary learning-based methods. All of these methods require at least one pair of observed coarse- and fine-resolution images for training and a coarse-resolution image at prediction date as input data. The output of spatio-temporal fusion methods is a synthetic fine-resolution image at prediction date. All spatio-temporal fusion methods use spatial information from the input fine-resolution images and temporal information from the coarse-resolution images. Unfortunately

existing spatio-temporal fusion methods cannot achieve satisfactory results in accurately predicting land-cover type change with only one pair of fine-coarse prior images. Thus

spatio-temporal satellite image-fusion method based on linear model is proposed to improve the prediction capacity and accuracy

especially for complex changed landscapes.

Method

2

The temporal model is assumed to be independent of sensors

and a linear relationship is used to represent the temporal model between images acquired on different dates. Therefore

the spatio-temporal fusion is transformed into estimating parameters of the temporal model. To accurately capture earth surface change during the period between the input and prediction dates

we carefully analyzed the reasons for the temporal change

and then the temporal change was divided into two types:phonological and land cover type. The former is mainly caused by differences in atmospheric condition

solar angle at different dates

and is global and flat. The latter is mainly caused by the change on the surface

and is local and abrupt. Therefore

parameters of the model from global and local perspectives were estimated. To accurately estimate the parameters

we need to search for similar pixels in the local window to ensure that the pixels used for parameter estimation satisfies spectral consistency. Moreover

considering that the land-cover type may change during the period

we find that the spatial distribution of similar pixels may change at different dates. Therefore

a multi-temporal search strategy is introduced to flexibly select appropriate neighboring pixels. Only pixels that have similar spectral information to the target pixel at both base date and prediction date are considered to be similar pixels

which eliminate the block effect of traditional algorithms. After searching similar pixels and solving the temporal model

the input fine resolution image was combined with the temporal model to predict fine image at target date. The aforementioned strategies make our method achieve good prediction results even if the earth surface changed drastically.

Result

2

We compared our model with two popular spatio-temporal fusion models:spatial and temporal adaptive reflectance fusion model (STARFM) and flexible spatiotemporal data fusion (FSDAF) method on two datasets. The experiment results show that our model outperforms all other methods in both datasets. In the first experiment

the dataset constitutes primarily phenological change. Therefore

all three methods achieve satisfactory results and our method achieves the best result. Quantitative comparisons show that our method achieves high correlation coefficient (CC)

peak signal-to-noise ratio (PSNR)

structural similarity (SSIM)

and lower root mean square error (RMSE). Compared with STARFM and FSDAF

our method increases CC by 0.25% and 0.28%

PSNR by 0.153 1 dB and 1.379 dB

SSIM by 0.79% and 2.3%

and decreases RMSE by 0.05% and 0.69%. In the second experiment

the dataset has undergone dramatic land-cover type change. Therefore

both STARFM and FSDAF have block effects at different levels visually. In quantitative assessment

compared with STARFM and FSDAF

our method increases CC by 6.64% and 3.26%

PSNR by 2.086 dB and 2.510 7 dB

SSIM by 11.76% and 11.2%

and decreases RMSE by 1.45% and 2.08%.

Conclusion

2

In this study

a spatio-temporal satellite image-fusion method based on linear model is proposed. This method uses a linear model to represent the temporal change. By analyzing the characteristics of the temporal change

the temporal model is constrained from local and global perspectives

and the solved model can represent the temporal change accurately. In addition

the method uses a multi-temporal similar pixel search strategy to search for similar pixels more flexibly

thereby eliminating the block effect of previous methods

fully utilizing spectral information in neighboring similar pixels

and improving the accuracy of prediction results. The experimental results show that in terms of visual comparison

compared with two popular spatiotemporal fusion methods

the proposed method can predict land-cover type change more accurately

and our findings are close to the true image. In the quantitative evaluation

our method improves CC

PSNR

RMSE

SSIM

and other indicators to varying degrees in each band.